Methods for Automatic Groove Identificatoin in 3D Bullet Land Scans
Kiegan Rice
Methods for Automatic Groove Identification
in 3D Bullet Land Scans
Kiegan Rice
Iowa State University
August 2nd, 2018
Background: Statistics Applied to Forensic Science
Validity of forensic evidence analysis has come under fire in the last decade
2016 PCAST report on feature-comparison methods
NIST Center of Excellence established in 2016
Background: Bullet Lands
When a gun is fired, the bullet is propelled forward through the barrel
As it travels down the barrel, it makes contact with parts of the barrel
Striations result from this contact
Striations are observed on land engraved areas (
lands
). Lands are separated by groove engraved areas (
grooves
).
Background: Collection of Land Data
Sensofar Confocal Light Microscope
Hamby set 44 - 35 bullets from 10 consecutively rifled Ruger barrels
Rescanned with CSAFE’s microscope
To use as ‘base’ set; methods to be tested on several other data sets
Have manual groove identifications for this whole set
Each pixel: .645 square microns
Each land is 2mm (2000 microns) wide
A scan of one bullet (6 individual lands) takes ~1 hour
Background: Land Surface
Background: Importance of Groove Removal
Bullet matching algorithm
Removing the underlying curved structure of land
Looking at remaining residuals
Deviations from the natural curve of the land
Background: Importance of Groove Removal
Robust LOESS
Groove Identification Process
Once we have the residuals, how do we decide where the cutoffs should be?
Ad hoc approach: define a cutoff
2*MAR (median absolute residual)
Comparing approaches
Difficult to determine a metric for accuracy
Number incorrectly identified doesn’t work
We will look at all the residuals in the areas between our predicted grooves and the manually identified grooves
For each land in the data set (208 total), sum up these residuals
Comparing approaches
The sums shown previously are calculated for each land and each method
Then, we compare the distributions of those values
Next steps
Moving away from ad hoc cut-off value
Two-class classification procedures for individual points within the data
Using residuals as predictor in model
Unbalanced response
Acknowledgments
All work was
sponsored
by CSAFE (Center for Statistics and Applications in Forensic Evidence), a NIST Center of Excellence
Work
advised
by Drs. Heike Hofmann and Ulrike Genschel of CSAFE/Iowa State University